9. Introduction
▪ Skills
– 40K+ standardized skills
– Members get endorsed on
skills
– Represent professional
expertise
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10. Introduction
▪ Unique challenges to LinkedIn expertise Search
– Scale: 400M members x 40K standardized skills
– Sparsity of skills in profiles
– Personalization
10
…
11. Reputation
Information a decision maker uses to make a
judgment on an entity with a record (*)
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(*) “Building web reputation systems”, Glass and Farmer, 2010
12. Skill Reputation Scores [BigData’15]
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▪ Decision Maker: searcher
▪ Record: Professional
career
▪ Skill reputation: member
expertise on a skill
▪ Judgment: Hire?
14. Estimating Skill Reputation
14
Endorse
profile
browsemap
? .85 .45
? ? .35
? .42 ?
? ? .05
Members
Skills
0.5 1
0.7 0
0 0.6
0.1 0
0.2 0.3 0.5
0.5 0.7 0.2
Members
Skills
Each row is a representation of a
member in latent space
Each column
represents a skill in
latent space
Matrix Factorization
15. Estimating Skill Reputation
15
Endorse
profile
browsemap
? .85 .45
? ? .35
? .42 ?
.02 ? ?
Members
Skills
0.5 1
0.7 0
0 0.6
0.1 0
0.2 0.3 0.5
0.5 0.7 0.2
Members
Skills
.6 .85 .45
.14 .21 .35
.3 .42 .12
.02 .03 .05
Members
Skills
Fill in unknown cells in
the original matrix
21. Challenges of Job Search
▪ “Hidden” structures
▪ Query only represents a small fraction of information need
–“San Francisco”, “software engineer”, “java”“Hidden” structures
▪ Job attractiveness varies on many aspects
–“Hot” titles: “data scientist”
–Top companies: Google, Facebook, etc.
–Trending skills: machine learning, big data, etc.,
–Location
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23. Expertise Homophily
▪ “Classic” homophily in social networks
–People tend to interact with similar ones
▪ Expertise homophily in job search
–Searcher tends to apply for jobs with similar expertise
–Apply rate of job results with overlapping skills is 2x higher
▪ Expertise: skill reputation scores
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24. Entity-faceted CTRs
▪ Job attractiveness
– Historical CTRs for individual jobs
– Challenge: job lifetime is short -> unreliable estimation
▪ Entity-faceted historical CTRs
– CTRs of jobs with standardized tile “data scientist”
– CTRs of jobs from company IBM
– CTRs of jobs requiring trending skill: machine learning, big data, etc.
▪ Advantages
– Alleviate data sparseness by grouping jobs by facets
– Resolve cold start problem
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25. Experiment Results
▪ Baseline
▪ All of the existing features except entity-aware ones
▪ Machine learned
▪ Optimized for the same objective function
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CTR Apply Rate
Improvement +11.3% +5.3%
32. Calibrate Scores across Verticals
▪ Relevance scores from vertical rankers are incomparable
▪ Construct composite features
People relevance score of searcher if result is People
f 1= ⎨0, otherwise
33. Searcher Intent
Searcher’s job seeking intent if result is job vertical cluster
Searcher’s job seeking intent if result is individual job
Searcher’s recruiting intent if result is people vertical cluster
Searcher’s recruiting intent if result is individual people
...
34. Take-Aways
▪ Text match is still important but not enough
▪ Advanced features based on semi-structured
data
– People search: skill reputation scores
– Job Search: expertise homophily
▪ Personalized Learning-to-Rank is crucial
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36. References
▪“Personalized Expertise Search at LinkedIn”, Ha-Thuc,
Venkataraman, Rodriguez, Sinha, Sundaram and Guo,
BigData, 2015
▪“Personalized Federated Search at LinkedIn”, Arya, Ha-
Thuc and Sinha, CIKM, 2015
▪“Learning to Rank Personalized Search Results in
Professional Networks”, Ha-Thuc and Sinha, SIGIR, 2016
▪“How to Get Them a Dream Job?”, Li, Arya, Ha-Thuc,
Sinha, KDD, 2016
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